1. Executive control network connectivity strength protects against relapse to cocaine use: Cocaine addiction, characterized by notoriously high relapse rates post-treatment. To address poor treatment (tx) outcomes we have turned to potential neural markers of relapse risk and examined resting state functional connectivity (rsFC) within and between the default mode network (DMN), salience network (SN) and executive control network (ECN), all implicated in craving, withdrawal and executive control deficits. Cocaine-dependent individuals (45) in their last week of residential tx and healthy controls (22) completed 6-min resting fMRI scans, Wisconsin Card Sorting Task, Continuous Performance Test and Cocaine Craving Questionnaire. Follow-up scans in 10 controls and 9 dependent participants were done 3-6mos later. In those abstinent up to day 30 post-tx (n=21), we found enhanced pre-discharge rsFC between the left ECN and both the right ECN and SN as well as between the right ECN and left ECN. Left ECN rsFC effects remained elevated 3-6mos among abstinent cocaine-dependent individuals. Relapse was related to fewer years of education and more years smoking but no other demographic, clinical, tx and neurocognitive characteristics. Findings suggest that interhemispheric ECN and ECN-SN connectivity strength may protect against relapse to cocaine use following tx. These patterns of enhanced interhemispheric network connectivity may reflect a greater capacity to engage executive control processes when faced with opportunities to use cocaine post-tx. 2. Salience and default mode network (DMN) dysregulation in chronic cocaine users predict treatment outcome: We investigated consequences of structural differences on rsFC in cocaine addiction and tested whether rsFC of the identified circuits predict relapse in an independent cohort. Subjects included 64 non-treatment-seeking cocaine users (NTSCUs) and 67 healthy controls and an independent cohort of 45 cocaine-dependent individuals scanned at the end of a 30-day residential tx program. Differences in cortical thickness and related rsFC between NTSCUs and controls were identified. Survival analysis, applying cortical thickness of the identified regions, rsFC of identified circuits and clinical characteristics to the treatment cohort, was used to predict relapse. Lower cortical thickness in bilateral insula and higher thickness in bilateral temporal pole were found in NTSCUs. Whole brain rsFC analyses with these 4 regions as seeds revealed eight weaker circuits including within the salience network (insula seeds) and between temporal pole and elements of the DMN in NTSCUs. Applying these circuits and clinical characteristics to the independent cohort, functional connectivity between right temporal pole and medial prefrontal cortex, combined with years of education, predicted relapse status at 150 days with 88% accuracy. Deficits in the salience network suggest an impaired ability to process physiologically salient events, while abnormalities in a temporal pole-medial prefrontal cortex circuit might speak to the social-emotional functional alterations. The involvement of the temporal pole-medial prefrontal cortex circuit in a model highly predictive of relapse highlights the importance of social-emotional functions in cocaine dependence and provides a potential underlying neural target for therapeutic interventions and for identifying those at high risk of relapse. 3. Brief rTMS delivered by H-Coil to a healthy volunteer induced delayed, transient hypomanic symptoms: A case report using an HAC-coil to modulate frontal lobe function as measured by fMRI in a 25yr old, TMS-nave, Asian female with no history contraindicated in TMS. She received an estimated 20 trains of increasing intensity levels ranging from 75% to 110% of RMT over 40 minutes. She reported that about 68 hours after rTMS, she experienced atypical elevated mood, excitability and verbosity lasting a few hours and resolved spontaneously the next day. The current case is, to our knowledge, the first reported case of hypomania in a healthy control after rTMS exposure using the HAC coil targeting the ACC. 4. Do Candidate Genes Affect the Brain's White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans: Susceptibility genes for psychiatric and neurological disorders - APOE, BDNF, CLU, CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, & ZNF804A - reportedly affect white matter (WM) microstructure in the brain, as assessed via diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain a small fraction of variance and are difficult to detect reliably in single cohort studies. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects from 11 cohorts worldwide to evaluate effects of 15 SNPs & examined their associations with WM microstructure. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Despite the very large sample, no significant associations remained after multiple-testing correction for the SNPs investigated. Suggestive associations (1.3x10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta- and mega-analyses revealed similar findings. Regardless of the approach, the SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure. 5. Brain networks associated with reward: This chapter describes brain networks related to reward processing. We will first consider the reward regions identified by decades of preclinical and more recently human research and the circuits that connect them. These will include the classical reward centers along with various cortical and subcortical structures that contribute to reward learning and reward-based decision-making. Then we explore how these nodes are identified in humans via fMRI and PET. We next discuss reward processing from a network-level perspective, including the methods by which structural and functional connectivity can be identified with MRI-based tools. In conclusion, we discuss functional connectivity networks that center on reward-related circuitry and networks that serve many behavioral purposes but affected by reward. Dysregulation of these reward networks circuitry in neuropsychiatric disorders will be explored using addiction as an exemplar. 6. Reward Circuitry and Drug Addiction: Dysregulation of reward circuitry is related to each of the three cyclical stages in the disease model of addiction: maintenance, abstinence, and relapse. Parsing reward circuitry is confounded due to the anatomical complexity of cortico-basal ganglia-thalamocortical loops, forward and backward projections within the circuit, and interactions between neurotransmitter systems. We introduce the neurobiology of the reward system, specifically highlighting nodes of the circuit followed by a review of current literature on reward circuitry dysregulation in addiction. Finally, we discuss biomarkers of addiction identified with neuroimaging that could lead to neuroprediction models and development of targets for effective interventions, such as non-invasive brain stimulation.